Journalisic Data Analysis

Do you see what I see?

The dictionary says: “A visualization is the process of representing abstract business or scientific data as images that can aid in understanding the meaning of the data.” But, unfortunately some visualizations do not aid in understanding the meaning of the data. Today, I am going to tell you something about (misleading) visualizations.

Some visuals are misleading as they give a wrong idea or impression. In the case of the advertisement of the McDonald (see above), the Big Mac looks more attractive in the advertisement than in reality. When you look closer to the infographic (see below) you will see that some features are also deceiving (e.g. the infographic appears to suggest that the travel industry is less dynamic in Europe than in the Asia-Pacific region).

These misleading attributes can be implemented by the designers in one of the four editorial layers (i.e., data, visual representation, textual annotations, and interactivity), and it can be seen as a strategy to pick the right layer to deceive. Following Alberto Cairo, the most misleading visuals are based on the following three tactics:

Hiding relevant data to highlight what benefits us;

Displaying too much data to obscure reality;

Using graphic forms in inappropriate ways.

Why are misleading visualizations bad?Jessica Hullman reported that even subtle changes in visualizations could influence responses. So, peoples’ interpretation and opinion could be altered towards an issue as a result of a misleading visual. In some instances this could have tremendous effect, for instance with emotional topics, such as racism, or decisive topics. The study of William C. Bradford suggested that over half of the people are visual learners. Supposedly, this is due because we understand visuals more easy as they affect us both cognitively (i.e., decode a text) and emotionally (i.e., strengthen our creative thinking). The study of Bruce I. Reiner reported that words are processed by our short-term memory whereas images go directly to our long-term memory. So, it seems that we remember the (wrong) interpretation extracted from a visual better than text. As a result, we could take action on the basis of false information.

Albert Cairo: “Charts, graphs, maps, and diagrams do not lie. People who design graphics do.”

Why do we use visualizations? We visualize to make stories or pieces of information more interesting and appealing (e.g. for commercial purposes, see McDonalds’ most finest hamburger), but also to make it more comprehensible and more easy to understand information {e.g. for informational purposes, see infographic}. We make misleading visuals to reach an objective that would be difficult or even impossible to obtain without ambiguous or false attributes. But, is it really a strategy? Do we make misleading visuals on purpose or could it be ‘just a mistake’? Please, look at the images below. Tell me in the comments if you thought they did it on purpose or if it was just by accident.

The visuals above are, in my opinion, both beautiful, but a little misleading. The first infographic shows the state individual income taxes collected per capita with some nice illustrated coins. But, a critical remark on this infographic could be that they only demonstrated the income taxes and they did not relate this to other interesting features, such as GDP. Another remark is that they didn’t elaborate on the source they used which makes it less accurate. The second visualization, which shows the Citeology, is beautiful, but it shows too much detail. This makes the infographic less functional.

How can we make good visualizations?David McCandless suggest that the key components of a data visualization are: interestingness, integrity, form and function. What does he means with this? One the one hand, the information in the visualization needs to be interesting (meaningful & relevant) and have high integrity (accuracy & consistency). On the other hand, the design of the visual needs to have form (beauty & structure) and function (easiness & usefulness). Alberto Cairo uses similar terms; he mentions in his video presentation that many visualizations today are beautiful, and even functional, but not particularly insightful.

Albert Cairo: “It is unacceptable to sacrifice the integrity of the data just to make an infographic pretty.”

Lastly, I want to give you some tips to make your visual worth watching:

Think of what your audience is seeking;

Identify the story you want to tell them;

Sketch before you produce your story.

On a final note: don’t get fooled by misleading visualizations, use your bullshit detector! Do you know how your bull-shit detector works?

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5 gedachtes over “Do you see what I see?”

Hello Genya! I agree with Albert Cairo that it is unacceptable to sacrifice the integrity of the data just to make an infographic pretty. But, I am not at all convinced that this is the trend today. As illustrated in the various examples you provide, misleading visualization are almost everywhere. To me, it’s not designers who should learn how to make the right visualizations! It is us that should learn how to resist and cope with them! Carry on the good work 🙂

I don’t agree on that it is just us who should learn how to resist and cope the misleading information, because I think the developers of an article or something should be honest and give the people the right information. if they make sure that they give the rigth information, we don’t have to worry about something anymore and just assume that what they say is true. ofcourse I’m aware that this will never happen, that they always will mislead us, but I don’t think you can just say that it is not their problem but ours.

your final blog had a real nice structure and was a good read 🙂 Personally, beauty to me goes hand in hand with function. The fact that they are a bit misleading, for me already takes away the beauty of it. If I want beauty, I go look at a nice painting. Informative data presented in a clear and transparant way is beauty to me and makes data more appealing. Too much effort is punt in the design rather than how the reader processes the information. I’d like to see it the other way around, but maybe i’m a bit boring 😉 . T

Hey Genya, nice final blog. I think designers should place information first. This is the essence of a visualization. To make the data interpretable. Of course, people want to look at something beautiful but that shouldn’t obstruct the informational part. Designers should stay objective and avoid misleading the audience. Something should be done about this.

Interesting blog to read! I think the key components that you mention are indeed interesting and good points to keep in mind when making a visualization. I think that a visualization should be informational and true in the first place. Then other factors can make a contribution to a more attractive visualization. That way, visualizations have a real function instead of misleading people.